Jian Li


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Controlled Text Generation Using Dictionary Prior in Variational Autoencoders
Xianghong Fang | Jian Li | Lifeng Shang | Xin Jiang | Qun Liu | Dit-Yan Yeung
Findings of the Association for Computational Linguistics: ACL 2022

While variational autoencoders (VAEs) have been widely applied in text generation tasks, they are troubled by two challenges: insufficient representation capacity and poor controllability. The former results from the posterior collapse and restrictive assumption, which impede better representation learning. The latter arises as continuous latent variables in traditional formulations hinder VAEs from interpretability and controllability. In this paper, we propose Dictionary Prior (DPrior), a new data-driven prior that enjoys the merits of expressivity and controllability. To facilitate controlled text generation with DPrior, we propose to employ contrastive learning to separate the latent space into several parts. Extensive experiments on both language modeling and controlled text generation demonstrate the effectiveness of the proposed approach.

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MINER: Multi-Interest Matching Network for News Recommendation
Jian Li | Jieming Zhu | Qiwei Bi | Guohao Cai | Lifeng Shang | Zhenhua Dong | Xin Jiang | Qun Liu
Findings of the Association for Computational Linguistics: ACL 2022

Personalized news recommendation is an essential technique to help users find interested news. Accurately matching user’s interests and candidate news is the key to news recommendation. Most existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest. However, user interest is usually diverse and may not be adequately modeled by a single user embedding. In this paper, we propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. We further propose a disagreement regularization to make the learned interests vectors more diverse. Moreover, we design a category-aware attention weighting strategy that incorporates the news category information as explicit interest signals into the attention mechanism. Extensive experiments on the MIND news recommendation benchmark demonstrate that our approach significantly outperforms existing state-of-the-art methods.

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MTRec: Multi-Task Learning over BERT for News Recommendation
Qiwei Bi | Jian Li | Lifeng Shang | Xin Jiang | Qun Liu | Hanfang Yang
Findings of the Association for Computational Linguistics: ACL 2022

Existing news recommendation methods usually learn news representations solely based on news titles. To sufficiently utilize other fields of news information such as category and entities, some methods treat each field as an additional feature and combine different feature vectors with attentive pooling. With the adoption of large pre-trained models like BERT in news recommendation, the above way to incorporate multi-field information may encounter challenges: the shallow feature encoding to compress the category and entity information is not compatible with the deep BERT encoding. In this paper, we propose a multi-task method to incorporate the multi-field information into BERT, which improves its news encoding capability. Besides, we modify the gradients of auxiliary tasks based on their gradient conflicts with the main task, which further boosts the model performance. Extensive experiments on the MIND news recommendation benchmark show the effectiveness of our approach.


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Natural Language Processing Meets Quantum Physics: A Survey and Categorization
Sixuan Wu | Jian Li | Peng Zhang | Yue Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent research has investigated quantum NLP, designing algorithms that process natural language in quantum computers, and also quantum-inspired algorithms that improve NLP performance on classical computers. In this survey, we review representative methods at the intersection of NLP and quantum physics in the past ten years, categorizing them according to the use of quantum theory, the linguistic targets that are modeled, and the downstream application. The literature review ends with a discussion on the key factors to the success that has been achieved by existing work, as well as challenges ahead, with the goal of better understanding the promises and further directions.


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Relation Extraction with Temporal Reasoning Based on Memory Augmented Distant Supervision
Jianhao Yan | Lin He | Ruqin Huang | Jian Li | Ying Liu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Distant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these examples to automatically generate the training data. However, the examples collected can be very noisy, and pose significant challenge for obtaining high quality labels. Previous work has made remarkable progress in predicting the relation from distant supervision, but typically ignores the temporal relations among those supervising instances. This paper formulates the problem of relation extraction with temporal reasoning and proposes a solution to predict whether two given entities participate in a relation at a given time spot. For this purpose, we construct a dataset called WIKI-TIME which additionally includes the valid period of a certain relation of two entities in the knowledge base. We propose a novel neural model to incorporate both the temporal information encoding and sequential reasoning. The experimental results show that, compared with the best of existing models, our model achieves better performance in both WIKI-TIME dataset and the well-studied NYT-10 dataset.

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Information Aggregation for Multi-Head Attention with Routing-by-Agreement
Jian Li | Baosong Yang | Zi-Yi Dou | Xing Wang | Michael R. Lyu | Zhaopeng Tu
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Multi-head attention is appealing for its ability to jointly extract different types of information from multiple representation subspaces. Concerning the information aggregation, a common practice is to use a concatenation followed by a linear transformation, which may not fully exploit the expressiveness of multi-head attention. In this work, we propose to improve the information aggregation for multi-head attention with a more powerful routing-by-agreement algorithm. Specifically, the routing algorithm iteratively updates the proportion of how much a part (i.e. the distinct information learned from a specific subspace) should be assigned to a whole (i.e. the final output representation), based on the agreement between parts and wholes. Experimental results on linguistic probing tasks and machine translation tasks prove the superiority of the advanced information aggregation over the standard linear transformation.


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Multi-Head Attention with Disagreement Regularization
Jian Li | Zhaopeng Tu | Baosong Yang | Michael R. Lyu | Tong Zhang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Multi-head attention is appealing for the ability to jointly attend to information from different representation subspaces at different positions. In this work, we introduce a disagreement regularization to explicitly encourage the diversity among multiple attention heads. Specifically, we propose three types of disagreement regularization, which respectively encourage the subspace, the attended positions, and the output representation associated with each attention head to be different from other heads. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness and universality of the proposed approach.